Lab 4

library(tidyverse)

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## v tibble  2.1.3     v dplyr   0.8.3
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## v readr   1.3.1     v forcats 0.4.0

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SNPs<- read.table("23andMe_complete.txt",header = TRUE, sep = "\t")

#EXERCISE 1


BarGraph1 <- ggplot(data = SNPs) + geom_bar(mapping = aes(x=chromosome), fill ="blue") + ggtitle("Hamza")
BarGraph1

#Exercise 2 


Graph3 <- ggplot(data = SNPs) + geom_bar(mapping = aes(x = chromosome, fill= genotype)) + ggtitle("Hamza") 
Graph3 + ylab("Genotype Count")+ xlab("Chromosome number")

mycolor<-c("AA"="green","AC"="green","AG"="green","AT"="green","CC"="green","CG"="green","CT"="green","D"="purple","DD"="purple","DI"="purple","GG"="green","GT"="green","I"="purple","II"="purple","TT" ="green","A"="blue","C"="blue","G"="blue","T"="blue","--"="black")     
Graph3 + scale_fill_manual(values=mycolor)

#Exercise 3

Genotype counts per chromosome

#Exercise 4

Total SNPs each chromosome

#Exercise 5


library(plotly)

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Graph5 <- ggplotly(ggplot(data = SNPs) + geom_bar(mapping = aes(x = chromosome, fill= genotype), position = "dodge") + facet_wrap(~genotype, ncol = 2, nrow = 15) + ggtitle("Total SNPs each chromosome") + ylab("Genotype Count") + xlab("Chromosome Numbers"), width = 1000, height = 1000)
Graph5[["x"]][["layout"]][["annotations"]][[2]][["x"]] <- -0.05
Graph5

#Excercise 6


library(DT)

R <- subset(SNPs, chromosome == "Y" )
datatable(R)

## Warning in instance$preRenderHook(instance): It seems your data is too big
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## rstudio.github.io/DT/server.html